numpy · ahaldane · Oct 20, 2016 · Aug 9, 2016 · Oct 19, 2016 · Oct 19, 2016
diff --git a/doc/release/1.12.0-notes.rst b/doc/release/1.12.0-notes.rst
@@ -249,6 +249,13 @@ context manager will work as expected. Additionally, it is possible
 to use the context manager as a decorator which can be useful when
 multiple tests give need to hide the same warning.
 
+New masked array functions ``ma.convolve`` and ``ma.correlate`` added
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+These functions wrapped the non-masked versions, but propagate through masked
+values. There are two different propagation modes. The default causes masked
+values to contaminate the result with masks, but the other mode only outputs
+masks if there is no alternative.
+
 
 Improvements
 ============

diff --git a/numpy/ma/core.py b/numpy/ma/core.py
@@ -56,10 +56,10 @@
     'argmax', 'argmin', 'argsort', 'around', 'array', 'asanyarray',
     'asarray', 'bitwise_and', 'bitwise_or', 'bitwise_xor', 'bool_', 'ceil',
     'choose', 'clip', 'common_fill_value', 'compress', 'compressed',
-    'concatenate', 'conjugate', 'copy', 'cos', 'cosh', 'count', 'cumprod',
-    'cumsum', 'default_fill_value', 'diag', 'diagonal', 'diff', 'divide',
-    'dump', 'dumps', 'empty', 'empty_like', 'equal', 'exp', 'expand_dims',
-    'fabs', 'filled', 'fix_invalid', 'flatten_mask',
+    'concatenate', 'conjugate', 'convolve', 'copy', 'correlate', 'cos', 'cosh',
+    'count', 'cumprod', 'cumsum', 'default_fill_value', 'diag', 'diagonal',
+    'diff', 'divide', 'dump', 'dumps', 'empty', 'empty_like', 'equal', 'exp',
+    'expand_dims', 'fabs', 'filled', 'fix_invalid', 'flatten_mask',
     'flatten_structured_array', 'floor', 'floor_divide', 'fmod',
     'frombuffer', 'fromflex', 'fromfunction', 'getdata', 'getmask',
     'getmaskarray', 'greater', 'greater_equal', 'harden_mask', 'hypot',
@@ -7366,6 +7366,81 @@ def outer(a, b):
 outerproduct = outer
 
 
+def _convolve_or_correlate(f, a, v, mode, propagate_mask):
+    """
+    Helper function for ma.correlate and ma.convolve
+    """
+    if propagate_mask:
+        # results which are contributed to by either item in any pair being invalid
+        mask = (
+            f(getmaskarray(a), np.ones(np.shape(v), dtype=np.bool), mode=mode)
+          | f(np.ones(np.shape(a), dtype=np.bool), getmaskarray(v), mode=mode)
+        )
+        data = f(getdata(a), getdata(v), mode=mode)
+    else:
+        # results which are not contributed to by any pair of valid elements
+        mask = ~f(~getmaskarray(a), ~getmaskarray(v))
+        data = f(filled(a, 0), filled(v, 0), mode=mode)
+
+    return masked_array(data, mask=mask)
+
+
+def correlate(a, v, mode='valid', propagate_mask=True):
+    """
+    Cross-correlation of two 1-dimensional sequences.
+
+    Parameters
+    ----------
+    a, v : array_like
+        Input sequences.
+    mode : {'valid', 'same', 'full'}, optional
+        Refer to the `np.convolve` docstring.  Note that the default
+        is 'valid', unlike `convolve`, which uses 'full'.
+    propagate_mask : bool
+        If True, then a result element is masked if any masked element contributes towards it.
+        If False, then a result element is only masked if no non-masked element
+        contribute towards it
+
+    Returns
+    -------
+    out : MaskedArray
+        Discrete cross-correlation of `a` and `v`.
+
+    See Also
+    --------
+    numpy.correlate : Equivalent function in the top-level NumPy module.
+    """
+    return _convolve_or_correlate(np.correlate, a, v, mode, propagate_mask)
+
+
+def convolve(a, v, mode='full', propagate_mask=True):
+    """
+    Returns the discrete, linear convolution of two one-dimensional sequences.
+
+    Parameters
+    ----------
+    a, v : array_like
+        Input sequences.
+    mode : {'valid', 'same', 'full'}, optional
+        Refer to the `np.convolve` docstring.
+    propagate_mask : bool
+        If True, then if any masked element is included in the sum for a result
+        element, then the result is masked.
+        If False, then the result element is only masked if no non-masked cells
+        contribute towards it
+
+    Returns
+    -------
+    out : MaskedArray
+        Discrete, linear convolution of `a` and `v`.
+
+    See Also
+    --------
+    numpy.convolve : Equivalent function in the top-level NumPy module.
+    """
+    return _convolve_or_correlate(np.convolve, a, v, mode, propagate_mask)
+
+
 def allequal(a, b, fill_value=True):
     """
     Return True if all entries of a and b are equal, using

diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py
@@ -4086,6 +4086,25 @@ def compressed(self):
         test = np.ma.compressed(M(shape=(0,1,2)))
         assert_equal(test, 42)
 
+    def test_convolve(self):
+        a = masked_equal(np.arange(5), 2)
+        b = np.array([1, 1])
+        test = np.ma.convolve(a, b)
+        assert_equal(test, masked_equal([0, 1, -1, -1, 7, 4], -1))
+
+        test = np.ma.convolve(a, b, propagate_mask=False)
+        assert_equal(test, masked_equal([0, 1, 1, 3, 7, 4], -1))
+
+        test = np.ma.convolve([1, 1], [1, 1, 1])
+        assert_equal(test, masked_equal([1, 2, 2, 1], -1))
+
+        a = [1, 1]
+        b = masked_equal([1, -1, -1, 1], -1)
+        test = np.ma.convolve(a, b, propagate_mask=False)
+        assert_equal(test, masked_equal([1, 1, -1, 1, 1], -1))
+        test = np.ma.convolve(a, b, propagate_mask=True)
+        assert_equal(test, masked_equal([-1, -1, -1, -1, -1], -1))
+
 
 class TestMaskedFields(TestCase):